Extracting/ing the condensed form of original text document by retaining its information and complete meaning is known as text summarization. The creation of manual summaries from large text documents is difficult and time-consuming for humans. Text summarization has become an important and challenging area in natural language processing. This paper presents a heuristic appraoch to extract a summary of e-news articles of the Telugu language. The method proposes new lexical parameter-based information extraction (IE) rules for scoring the sentences. Event score and Named Entity Score is a novel part in sentence scoring to identify the essential information in the text. Depending on the frequency of occurrence of event/named entites in the sentence and document, sentences are selected for summary. Data is collected from online news sources (i.e., Eenadu, Sakshi,Andhra Jyothi, Namaste Telangana) to experiment. The proposed method is compared with other techniques developed for Telugu text summarization. Evaluation metrics like precision, recall, and F1 score is used to measure the proposed method's performance. An extensive statistical and qualitative evaluation of the system's summaries has been conducted using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), a standard summary evaluation tool. The results showed improved performance compared to other methods.

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A Heuristic Approach for Telugu Text Summarization with Improved Sentence Ranking

. In this paper, we survey Text Summarization (TS) datasets in Indian Languages (ILs), which are also low-resource languages (LRLs). We seek to answer one primary question – is the pool of Indian Language Text Summarization (ILTS) dataset growing or is there a serious resource poverty? To answer the primary question, we pose two sub-questions that we seek about ILTS datasets first, what characteristics – format and domain do ILTS datasets have? Second, how different are those characteristics of ILTS datasets from high-resource languages (HRLs) particularly English. The survey of ILTS and English datasets reveals two similarities and one contrast. The two similarities are – first, the domain of dataset commonly is news (Hermann et al., 2015). The second similarity is the format of the dataset which is both extractive and abstractive. The contrast is in how the research in dataset development has progressed. ILs face a slow speed of development and public release of datasets as compared with English. We conclude that the relatively lower number of ILTS datasets is because of two reasons- first, absence of a dedicated forum for developing TS tools; and second, lack of shareable standard datasets in the public domain.

An Overview of Indian Language Datasets used for Text Summarization

Deep learning based sequence to sequence model for abstractive telugu text summarization

Text summarization is one of the core concepts of natural language processing techniques. Text summarization is a process of producing a quality summary statements from a document which has a set of raw information, without losing or altering any valid information. We have developed a Kannada text summarization model using page ranking algorithm. The sentences are represented as vectors; similarity between the sentences is calculated using cosine similarity and a similarity graph is generated. The graph is fed as an input to the text ranking algorithm. The text ranking algorithm will then prioritize the sentences, and this output is considered as the summary of the Kannada document. The proposed model is evaluated by Rouge Scores; ROUGE 1 and L average F1 values are 65% and 64% respectively. ROUGE-2 average F1 score is near to 60%. This indicates the performance of the proposed text summarization model for the Kannada language is better and can further be improved with different Kannada datasets. 

Extractive Text Summarization of Kannada Text Documents Using Page Ranking Technique

Telugu is a common south Indian language communicated by more than 74 million people in India. Today with the advent of the Internet huge volume of text in the form of news articles, editorials, blogs etc. are available for access to the users. Summarization of these contents becomes an essential requirement to enable faster and efficient search of such documents. Manual text summarization consumes a plenty of time, effort, and money, and it's even impractical when there's a lot of text. Since the 1950s, researchers are working to develop various ATS techniques. There are three types of ATS approaches: extractive summarization, abstractive summarization and hybrid. The extractive method picks out the most important sentences from the input documents and combined them to produce the summary. The abstractive method translates the input documents into an intermediate representation before generating a description of sentences that differ from the originals. The extractive and abstractive methods are combined in the hybrid approach. A review and study of text summarization techniques developed for Telugu language documents are presented in this paper.

Automatic Text Summarization for Telugu Language

ive text summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming for humans to summarize manually large documents of text. Much of work in abstractive text summarization is being done in English, and almost no significant work has been reported in Telugu abstractive text summarization. So, we would like to propose an abstractive text summarization approach for Telugu language using deep learning. In this paper, we are proposing an abstractive text summarization deep learning model for Telugu language. The proposed architecture is based on encoder–decoder sequential models with attention mechanism. We have applied this model on manually created dataset to generate a one sentence summary of the source text and have got good results measured qualitatively.

B. Aravindh Gowtham

Papers

Neural Abstractive Text Summarizer for Telugu Language